Customer Learning Notes
SkillDocs & knowledgeTurn customer conversation notes, interview transcripts, call summaries, CRM snippets, or research notes into shared team learning and next questions. Use when synthesizing raw customer notes, avoiding founder interpretation bottlenecks, extracting quotes and signals, updating beliefs, or deciding what to ask next.
Use Customer Learning Notes in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Customer Learning Notes and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Customer Learning Notes skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by hashgraph-online/awesome-codex-plugins in plugins/LVTD-LLC/skills/skills/customer-learning-notes/SKILL.md and read by Ahel’s review.
Use this skill after customer conversations to turn raw notes into team-readable evidence. Good notes make it harder to misremember, overfit, or let one founder become the sole source of customer truth.
Source Traceability
Primary source: The Mom Test by Rob Fitzpatrick, especially chapter 8 and the conclusion. Guidance is paraphrased for this MIT repo; authoring notes used converted EPUB lines 3613-4445.
Signal Taxonomy
Use these labels when synthesizing notes:
| Label | Meaning |
|---|---|
| Pain | Problem, obstacle, annoyance, risk, or cost |
| Goal | Desired outcome, job to be done, or priority |
| Workaround | Current manual process, tool stack, hack, or substitute |
| Money | Budget, cost, value, purchase process, or decision owner |
| Person | Specific stakeholder, competitor, team, buyer, or intro lead |
| Feature | Request, buying criterion, integration need, or implementation clue |
| Emotion | Strong excitement, anger, embarrassment, fear, or skepticism |
| Follow-up | Promise, task, intro, research item, or next step |
Synthesis Workflow
- Preserve concrete facts separately from interpretation.
- Pull out short, useful quotes only when they are needed for traceability, positioning, or internal alignment.
- Tag signals using the taxonomy above.
- Group evidence by segment, problem, workaround, budget, and commitment.
- Identify contradictions and mixed-segment noise.
- Update beliefs, risks, and the next three questions.
- Recommend whether to continue, narrow the segment, ask for commitments, or move to building/testing.
Confidence Levels
| Level | Use When |
|---|---|
| High | Repeated behavior from a focused segment, with concrete cost or commitment. |
| Medium | Specific evidence from a few good-fit conversations. |
| Low | One-off quotes, mixed segments, opinions, or weakly anchored claims. |
Output Format
# Customer Learning Synthesis
## Source Notes
- Conversations:
- Segment:
- Date range:
## Evidence
| Signal | Evidence | Segment | Confidence | Implication |
|--------|----------|---------|------------|-------------|
## Belief Updates
- Stronger / weaker / new / rejected:
## Decisions
- Product, segment, positioning, sales or access:
## Next 3 Questions
1. [Question]
2. [Question]
3. [Question]
Workflow
Use workflows/synthesize-conversation-notes.md when the user provides raw
notes, transcripts, call summaries, or interview excerpts.
Quality Bar
- Do not summarize notes into vibes.
- Do not let one loud quote outweigh repeated behavior from a focused segment.
- Do not mix segments without labeling them.
- Do not treat notes as useful until they have been reviewed and turned into updated beliefs or decisions.
Signals
- GitHub stars
- 1k
- Forks
- 316
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
customer-learning-notes- Source
- github.com/hashgraph-online/awesome-codex-plugins
github.com/hashgraph-online/awesome-codex-plugins
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